Overview

Dataset statistics

Number of variables25
Number of observations129547
Missing cells225709
Missing cells (%)7.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.7 MiB
Average record size in memory200.0 B

Variable types

Numeric11
Text4
Categorical9
DateTime1

Alerts

ACC_ID is highly overall correlated with OBJECTIDHigh correlation
EVENT_NATURE is highly overall correlated with EVENT_TYPEHigh correlation
EVENT_TYPE is highly overall correlated with EVENT_NATUREHigh correlation
LATITUDE is highly overall correlated with YHigh correlation
LONGITUDE is highly overall correlated with XHigh correlation
OBJECTID is highly overall correlated with ACC_IDHigh correlation
X is highly overall correlated with LONGITUDEHigh correlation
Y is highly overall correlated with LATITUDEHigh correlation
TOTAL_TRUCK_INVOLVED is highly imbalanced (85.4%)Imbalance
TOTAL_HEAVY_TRUCK_INVOLVED is highly imbalanced (93.4%)Imbalance
TOTAL_MOTOR_CYCLE_INVOLVED is highly imbalanced (88.8%)Imbalance
TOTAL_PEDESTRIANS_INVOLVED is highly imbalanced (94.5%)Imbalance
INTERSECTION_NO has 60975 (47.1%) missing valuesMissing
INTERSECTION_DESC has 60975 (47.1%) missing valuesMissing
EVENT_NATURE has 7767 (6.0%) missing valuesMissing
EVENT_TYPE has 95881 (74.0%) missing valuesMissing
OBJECTID is uniformly distributedUniform
OBJECTID has unique valuesUnique
ACC_ID has unique valuesUnique
SLK has 3825 (3.0%) zerosZeros
TOTAL_BIKE_INVOLVED has 126868 (97.9%) zerosZeros
TOTAL_OTHER_VEHICLES_INVOLVED has 8156 (6.3%) zerosZeros

Reproduction

Analysis started2023-12-14 14:18:48.576826
Analysis finished2023-12-14 14:19:09.763634
Duration21.19 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

X
Real number (ℝ)

HIGH CORRELATION 

Distinct60258
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.03808
Minimum105.66151
Maximum128.91782
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1012.2 KiB
2023-12-14T09:19:09.938630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum105.66151
5-th percentile115.69315
Q1115.79621
median115.85523
Q3115.93419
95-th percentile116.83737
Maximum128.91782
Range23.256317
Interquartile range (IQR)0.137972

Descriptive statistics

Standard deviation1.0616833
Coefficient of variation (CV)0.0091494393
Kurtosis52.381959
Mean116.03808
Median Absolute Deviation (MAD)0.068805
Skewness6.4837667
Sum15032385
Variance1.1271715
MonotonicityNot monotonic
2023-12-14T09:19:09.996302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
115.730422 273
 
0.2%
115.812809 238
 
0.2%
115.938151 202
 
0.2%
115.997043 169
 
0.1%
115.917819 164
 
0.1%
116.014046 161
 
0.1%
115.945186 156
 
0.1%
115.83769 153
 
0.1%
115.854355 151
 
0.1%
115.842203 151
 
0.1%
Other values (60248) 127729
98.6%
ValueCountFrequency (%)
105.661505 1
< 0.1%
113.402441 1
< 0.1%
113.457191 1
< 0.1%
113.532843 1
< 0.1%
113.534861 1
< 0.1%
113.538132 2
< 0.1%
113.540271 1
< 0.1%
113.552467 1
< 0.1%
113.56344 1
< 0.1%
113.568052 1
< 0.1%
ValueCountFrequency (%)
128.917822 1
< 0.1%
128.917412 1
< 0.1%
128.875419 1
< 0.1%
128.857925 1
< 0.1%
128.85628 1
< 0.1%
128.841068 1
< 0.1%
128.812538 1
< 0.1%
128.79637 1
< 0.1%
128.78655 1
< 0.1%
128.777135 1
< 0.1%

Y
Real number (ℝ)

HIGH CORRELATION 

Distinct61903
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-31.842453
Minimum-35.08765
Maximum-10.449186
Zeros0
Zeros (%)0.0%
Negative129547
Negative (%)100.0%
Memory size1012.2 KiB
2023-12-14T09:19:10.054597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-35.08765
5-th percentile-33.336035
Q1-32.076102
median-31.966486
Q3-31.889587
95-th percentile-31.597344
Maximum-10.449186
Range24.638464
Interquartile range (IQR)0.186515

Descriptive statistics

Standard deviation1.7225462
Coefficient of variation (CV)-0.054095901
Kurtosis44.585816
Mean-31.842453
Median Absolute Deviation (MAD)0.09425
Skewness6.1723529
Sum-4125094.3
Variance2.9671654
MonotonicityNot monotonic
2023-12-14T09:19:10.112741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-31.670532 273
 
0.2%
-31.857112 238
 
0.2%
-32.062188 202
 
0.2%
-32.008137 169
 
0.1%
-32.088901 164
 
0.1%
-31.864203 161
 
0.1%
-32.026872 156
 
0.1%
-31.888965 152
 
0.1%
-32.065309 151
 
0.1%
-31.820966 151
 
0.1%
Other values (61893) 127730
98.6%
ValueCountFrequency (%)
-35.08765 1
< 0.1%
-35.083258 1
< 0.1%
-35.08166 1
< 0.1%
-35.078825 1
< 0.1%
-35.075294 1
< 0.1%
-35.069633 1
< 0.1%
-35.068295 1
< 0.1%
-35.067451 1
< 0.1%
-35.065815 1
< 0.1%
-35.062846 1
< 0.1%
ValueCountFrequency (%)
-10.449186 1
< 0.1%
-14.277027 1
< 0.1%
-14.291527 1
< 0.1%
-14.362412 1
< 0.1%
-14.409247 1
< 0.1%
-15.463731 1
< 0.1%
-15.471212 1
< 0.1%
-15.471334 1
< 0.1%
-15.485425 1
< 0.1%
-15.539414 1
< 0.1%

OBJECTID
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct129547
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45751815
Minimum45687042
Maximum45816588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1012.2 KiB
2023-12-14T09:19:10.169882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum45687042
5-th percentile45693519
Q145719428
median45751815
Q345784202
95-th percentile45810111
Maximum45816588
Range129546
Interquartile range (IQR)64773

Descriptive statistics

Standard deviation37397.142
Coefficient of variation (CV)0.00081739144
Kurtosis-1.2
Mean45751815
Median Absolute Deviation (MAD)32387
Skewness0
Sum5.9270104 × 1012
Variance1.3985462 × 109
MonotonicityStrictly increasing
2023-12-14T09:19:10.232262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45687042 1
 
< 0.1%
45773401 1
 
< 0.1%
45773414 1
 
< 0.1%
45773413 1
 
< 0.1%
45773412 1
 
< 0.1%
45773411 1
 
< 0.1%
45773410 1
 
< 0.1%
45773409 1
 
< 0.1%
45773408 1
 
< 0.1%
45773407 1
 
< 0.1%
Other values (129537) 129537
> 99.9%
ValueCountFrequency (%)
45687042 1
< 0.1%
45687043 1
< 0.1%
45687044 1
< 0.1%
45687045 1
< 0.1%
45687046 1
< 0.1%
45687047 1
< 0.1%
45687048 1
< 0.1%
45687049 1
< 0.1%
45687050 1
< 0.1%
45687051 1
< 0.1%
ValueCountFrequency (%)
45816588 1
< 0.1%
45816587 1
< 0.1%
45816586 1
< 0.1%
45816585 1
< 0.1%
45816584 1
< 0.1%
45816583 1
< 0.1%
45816582 1
< 0.1%
45816581 1
< 0.1%
45816580 1
< 0.1%
45816579 1
< 0.1%

ACC_ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct129547
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10265707
Minimum9675718
Maximum11001714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1012.2 KiB
2023-12-14T09:19:10.293916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum9675718
5-th percentile9762582.3
Q19981088
median10262045
Q310550649
95-th percentile10766967
Maximum11001714
Range1325996
Interquartile range (IQR)569561

Descriptive statistics

Standard deviation325476.33
Coefficient of variation (CV)0.031705203
Kurtosis-1.2296515
Mean10265707
Median Absolute Deviation (MAD)284728
Skewness0.00052544485
Sum1.3298916 × 1012
Variance1.0593484 × 1011
MonotonicityNot monotonic
2023-12-14T09:19:10.356428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9747180 1
 
< 0.1%
10483878 1
 
< 0.1%
10483965 1
 
< 0.1%
10483958 1
 
< 0.1%
10483952 1
 
< 0.1%
10483946 1
 
< 0.1%
10483940 1
 
< 0.1%
10483934 1
 
< 0.1%
10483928 1
 
< 0.1%
10483918 1
 
< 0.1%
Other values (129537) 129537
> 99.9%
ValueCountFrequency (%)
9675718 1
< 0.1%
9675749 1
< 0.1%
9675796 1
< 0.1%
9676124 1
< 0.1%
9676287 1
< 0.1%
9676293 1
< 0.1%
9676297 1
< 0.1%
9676302 1
< 0.1%
9676307 1
< 0.1%
9676315 1
< 0.1%
ValueCountFrequency (%)
11001714 1
< 0.1%
10997214 1
< 0.1%
10991214 1
< 0.1%
10979019 1
< 0.1%
10972358 1
< 0.1%
10967888 1
< 0.1%
10964230 1
< 0.1%
10960561 1
< 0.1%
10960122 1
< 0.1%
10956091 1
< 0.1%
Distinct12427
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size1012.2 KiB
2023-12-14T09:19:10.530852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length5.7701915
Min length4

Characters and Unicode

Total characters747511
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6700 ?
Unique (%)5.2%

Sample

1st row1050835
2nd rowH002
3rd row1310334
4th rowH030
5th row1250059
ValueCountFrequency (%)
h015 3968
 
3.1%
h001 3723
 
2.9%
h016 3159
 
2.4%
h005 3142
 
2.4%
h017 2724
 
2.1%
h035 2617
 
2.0%
h002 2483
 
1.9%
h013 1864
 
1.4%
h012 1851
 
1.4%
h018 1603
 
1.2%
Other values (12417) 102413
79.1%
2023-12-14T09:19:10.777399image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 228186
30.5%
1 157836
21.1%
2 76157
 
10.2%
H 50339
 
6.7%
5 43905
 
5.9%
3 43063
 
5.8%
4 36656
 
4.9%
6 30139
 
4.0%
7 27855
 
3.7%
9 26220
 
3.5%
Other values (4) 27155
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 693856
92.8%
Uppercase Letter 53655
 
7.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 228186
32.9%
1 157836
22.7%
2 76157
 
11.0%
5 43905
 
6.3%
3 43063
 
6.2%
4 36656
 
5.3%
6 30139
 
4.3%
7 27855
 
4.0%
9 26220
 
3.8%
8 23839
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
H 50339
93.8%
M 2759
 
5.1%
Z 549
 
1.0%
P 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 693856
92.8%
Latin 53655
 
7.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 228186
32.9%
1 157836
22.7%
2 76157
 
11.0%
5 43905
 
6.3%
3 43063
 
6.2%
4 36656
 
5.3%
6 30139
 
4.3%
7 27855
 
4.0%
9 26220
 
3.8%
8 23839
 
3.4%
Latin
ValueCountFrequency (%)
H 50339
93.8%
M 2759
 
5.1%
Z 549
 
1.0%
P 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 747511
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 228186
30.5%
1 157836
21.1%
2 76157
 
10.2%
H 50339
 
6.7%
5 43905
 
5.9%
3 43063
 
5.8%
4 36656
 
4.9%
6 30139
 
4.0%
7 27855
 
3.7%
9 26220
 
3.5%
Other values (4) 27155
 
3.6%
Distinct10550
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size1012.2 KiB
2023-12-14T09:19:10.993620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length68
Median length63
Mean length12.798112
Min length4

Characters and Unicode

Total characters1657957
Distinct characters73
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5516 ?
Unique (%)4.3%

Sample

1st rowLitoria Dr
2nd rowMelville Mandurah Hwy
3rd rowForrest Rd
4th rowPort Beach Rd
5th rowAlexander Dr
ValueCountFrequency (%)
rd 35831
 
11.6%
hwy 29919
 
9.7%
st 26667
 
8.6%
fwy 10293
 
3.3%
av 9537
 
3.1%
dr 8243
 
2.7%
mitchell 4913
 
1.6%
kwinana 4645
 
1.5%
great 4340
 
1.4%
albany 4139
 
1.3%
Other values (7875) 171047
55.3%
2023-12-14T09:19:11.280676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
180028
 
10.9%
a 112331
 
6.8%
e 109334
 
6.6%
n 103463
 
6.2%
r 99921
 
6.0%
t 92833
 
5.6%
o 89994
 
5.4%
l 72504
 
4.4%
d 70299
 
4.2%
i 67910
 
4.1%
Other values (63) 659340
39.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1166309
70.3%
Uppercase Letter 301882
 
18.2%
Space Separator 180028
 
10.9%
Open Punctuation 4239
 
0.3%
Close Punctuation 4239
 
0.3%
Dash Punctuation 867
 
0.1%
Decimal Number 202
 
< 0.1%
Other Punctuation 191
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 112331
 
9.6%
e 109334
 
9.4%
n 103463
 
8.9%
r 99921
 
8.6%
t 92833
 
8.0%
o 89994
 
7.7%
l 72504
 
6.2%
d 70299
 
6.0%
i 67910
 
5.8%
y 61059
 
5.2%
Other values (16) 286661
24.6%
Uppercase Letter
ValueCountFrequency (%)
R 47318
15.7%
S 41006
13.6%
H 36312
12.0%
M 21365
 
7.1%
A 18637
 
6.2%
W 16429
 
5.4%
F 14811
 
4.9%
B 14071
 
4.7%
C 12928
 
4.3%
G 11855
 
3.9%
Other values (16) 67150
22.2%
Decimal Number
ValueCountFrequency (%)
1 49
24.3%
0 36
17.8%
5 36
17.8%
3 18
 
8.9%
4 17
 
8.4%
2 15
 
7.4%
9 15
 
7.4%
6 8
 
4.0%
7 6
 
3.0%
8 2
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 89
46.6%
' 55
28.8%
& 35
 
18.3%
/ 11
 
5.8%
: 1
 
0.5%
Open Punctuation
ValueCountFrequency (%)
( 4238
> 99.9%
[ 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 4238
> 99.9%
] 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
180028
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 867
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1468191
88.6%
Common 189766
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 112331
 
7.7%
e 109334
 
7.4%
n 103463
 
7.0%
r 99921
 
6.8%
t 92833
 
6.3%
o 89994
 
6.1%
l 72504
 
4.9%
d 70299
 
4.8%
i 67910
 
4.6%
y 61059
 
4.2%
Other values (42) 588543
40.1%
Common
ValueCountFrequency (%)
180028
94.9%
( 4238
 
2.2%
) 4238
 
2.2%
- 867
 
0.5%
. 89
 
< 0.1%
' 55
 
< 0.1%
1 49
 
< 0.1%
0 36
 
< 0.1%
5 36
 
< 0.1%
& 35
 
< 0.1%
Other values (11) 95
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1657957
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
180028
 
10.9%
a 112331
 
6.8%
e 109334
 
6.6%
n 103463
 
6.2%
r 99921
 
6.0%
t 92833
 
5.6%
o 89994
 
5.4%
l 72504
 
4.4%
d 70299
 
4.2%
i 67910
 
4.1%
Other values (63) 659340
39.8%
Distinct10607
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size1012.2 KiB
2023-12-14T09:19:11.489520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length68
Median length64
Mean length12.090315
Min length5

Characters and Unicode

Total characters1566264
Distinct characters73
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5518 ?
Unique (%)4.3%

Sample

1st rowLitoria Dr
2nd rowRockingham Rd
3rd rowForrest Rd
4th rowCurtin Av
5th rowAlexander Dr
ValueCountFrequency (%)
rd 42719
 
14.2%
st 27926
 
9.3%
hwy 23276
 
7.7%
fwy 10274
 
3.4%
av 10217
 
3.4%
dr 9466
 
3.2%
mitchell 4913
 
1.6%
kwinana 4644
 
1.5%
great 4058
 
1.4%
to 3749
 
1.2%
Other values (7907) 159241
53.0%
2023-12-14T09:19:11.767635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
170937
 
10.9%
a 103201
 
6.6%
n 99256
 
6.3%
e 95592
 
6.1%
r 95112
 
6.1%
t 91144
 
5.8%
o 88796
 
5.7%
d 74883
 
4.8%
i 61344
 
3.9%
l 59740
 
3.8%
Other values (63) 626259
40.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1092186
69.7%
Uppercase Letter 293130
 
18.7%
Space Separator 170937
 
10.9%
Close Punctuation 4315
 
0.3%
Open Punctuation 4315
 
0.3%
Dash Punctuation 948
 
0.1%
Decimal Number 231
 
< 0.1%
Other Punctuation 202
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 103201
 
9.4%
n 99256
 
9.1%
e 95592
 
8.8%
r 95112
 
8.7%
t 91144
 
8.3%
o 88796
 
8.1%
d 74883
 
6.9%
i 61344
 
5.6%
l 59740
 
5.5%
y 52851
 
4.8%
Other values (16) 270267
24.7%
Uppercase Letter
ValueCountFrequency (%)
R 53321
18.2%
S 42309
14.4%
H 29857
10.2%
A 18708
 
6.4%
M 16734
 
5.7%
F 14731
 
5.0%
W 14302
 
4.9%
B 14114
 
4.8%
C 12968
 
4.4%
D 11636
 
4.0%
Other values (16) 64450
22.0%
Decimal Number
ValueCountFrequency (%)
1 60
26.0%
0 46
19.9%
5 38
16.5%
3 22
 
9.5%
2 18
 
7.8%
4 17
 
7.4%
9 15
 
6.5%
6 9
 
3.9%
7 4
 
1.7%
8 2
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 95
47.0%
' 55
27.2%
& 36
 
17.8%
/ 15
 
7.4%
: 1
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 4314
> 99.9%
] 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 4314
> 99.9%
[ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
170937
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 948
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1385316
88.4%
Common 180948
 
11.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 103201
 
7.4%
n 99256
 
7.2%
e 95592
 
6.9%
r 95112
 
6.9%
t 91144
 
6.6%
o 88796
 
6.4%
d 74883
 
5.4%
i 61344
 
4.4%
l 59740
 
4.3%
R 53321
 
3.8%
Other values (42) 562927
40.6%
Common
ValueCountFrequency (%)
170937
94.5%
) 4314
 
2.4%
( 4314
 
2.4%
- 948
 
0.5%
. 95
 
0.1%
1 60
 
< 0.1%
' 55
 
< 0.1%
0 46
 
< 0.1%
5 38
 
< 0.1%
& 36
 
< 0.1%
Other values (11) 105
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1566264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
170937
 
10.9%
a 103201
 
6.6%
n 99256
 
6.3%
e 95592
 
6.1%
r 95112
 
6.1%
t 91144
 
5.8%
o 88796
 
5.7%
d 74883
 
4.8%
i 61344
 
3.9%
l 59740
 
3.8%
Other values (63) 626259
40.0%

CWAY
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1012.2 KiB
S
71703 
L
45085 
R
12759 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129547
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowR
3rd rowS
4th rowS
5th rowL

Common Values

ValueCountFrequency (%)
S 71703
55.3%
L 45085
34.8%
R 12759
 
9.8%

Length

2023-12-14T09:19:11.847695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T09:19:11.893306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
s 71703
55.3%
l 45085
34.8%
r 12759
 
9.8%

Most occurring characters

ValueCountFrequency (%)
S 71703
55.3%
L 45085
34.8%
R 12759
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 129547
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 71703
55.3%
L 45085
34.8%
R 12759
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 129547
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 71703
55.3%
L 45085
34.8%
R 12759
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129547
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 71703
55.3%
L 45085
34.8%
R 12759
 
9.8%

SLK
Real number (ℝ)

ZEROS 

Distinct8313
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.527288
Minimum0
Maximum3188.66
Zeros3825
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1012.2 KiB
2023-12-14T09:19:11.942367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06
Q10.5
median1.91
Q37.85
95-th percentile48.33
Maximum3188.66
Range3188.66
Interquartile range (IQR)7.35

Descriptive statistics

Standard deviation110.33246
Coefficient of variation (CV)6.2948965
Kurtosis384.42116
Mean17.527288
Median Absolute Deviation (MAD)1.72
Skewness17.654492
Sum2270607.6
Variance12173.252
MonotonicityNot monotonic
2023-12-14T09:19:12.000342image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3825
 
3.0%
0.1 888
 
0.7%
0.11 846
 
0.7%
0.09 837
 
0.6%
0.12 831
 
0.6%
0.06 814
 
0.6%
0.05 765
 
0.6%
0.08 753
 
0.6%
0.19 741
 
0.6%
0.07 731
 
0.6%
Other values (8303) 118516
91.5%
ValueCountFrequency (%)
0 3825
3.0%
0.01 43
 
< 0.1%
0.02 457
 
0.4%
0.03 465
 
0.4%
0.04 617
 
0.5%
0.05 765
 
0.6%
0.06 814
 
0.6%
0.07 731
 
0.6%
0.08 753
 
0.6%
0.09 837
 
0.6%
ValueCountFrequency (%)
3188.66 1
< 0.1%
3177.42 1
< 0.1%
3170.16 1
< 0.1%
3164.64 1
< 0.1%
3160.14 1
< 0.1%
3155.06 1
< 0.1%
3154.32 1
< 0.1%
3142.03 1
< 0.1%
3135 1
< 0.1%
3131.94 1
< 0.1%

INTERSECTION_NO
Real number (ℝ)

MISSING 

Distinct12981
Distinct (%)18.9%
Missing60975
Missing (%)47.1%
Infinite0
Infinite (%)0.0%
Mean60435.671
Minimum1
Maximum287343
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1012.2 KiB
2023-12-14T09:19:12.057869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4147
Q119110.75
median50693
Q367701
95-th percentile179002
Maximum287343
Range287342
Interquartile range (IQR)48590.25

Descriptive statistics

Standard deviation53896.287
Coefficient of variation (CV)0.89179594
Kurtosis2.9768502
Mean60435.671
Median Absolute Deviation (MAD)18294
Skewness1.7139364
Sum4.1441949 × 109
Variance2.9048097 × 109
MonotonicityNot monotonic
2023-12-14T09:19:12.114715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
228503 273
 
0.2%
3717 238
 
0.2%
43729 202
 
0.2%
4547 169
 
0.1%
43737 164
 
0.1%
4147 161
 
0.1%
4255 156
 
0.1%
55048 152
 
0.1%
90889 151
 
0.1%
47055 151
 
0.1%
Other values (12971) 66755
51.5%
(Missing) 60975
47.1%
ValueCountFrequency (%)
1 3
< 0.1%
14 1
 
< 0.1%
48 2
< 0.1%
50 1
 
< 0.1%
62 1
 
< 0.1%
63 1
 
< 0.1%
66 2
< 0.1%
68 1
 
< 0.1%
69 2
< 0.1%
70 1
 
< 0.1%
ValueCountFrequency (%)
287343 1
< 0.1%
287274 1
< 0.1%
287272 1
< 0.1%
287270 1
< 0.1%
287142 1
< 0.1%
287111 1
< 0.1%
287088 1
< 0.1%
287050 1
< 0.1%
286673 1
< 0.1%
286460 1
< 0.1%

INTERSECTION_DESC
Text

MISSING 

Distinct12770
Distinct (%)18.6%
Missing60975
Missing (%)47.1%
Memory size1012.2 KiB
2023-12-14T09:19:12.264484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length283
Median length146
Mean length35.583839
Min length7

Characters and Unicode

Total characters2440055
Distinct characters73
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6495 ?
Unique (%)9.5%

Sample

1st rowForrest Rd & Gregory Av
2nd rowAlexander Dr & Rookwood St & Melrose Cr
3rd rowKaruah Wy & Hepburn Av
4th rowAlbany Hwy & Kenwick Link & William St
5th rowNorth Rd & Barnesby Dr
ValueCountFrequency (%)
93812
 
18.7%
rd 51467
 
10.3%
st 42135
 
8.4%
hwy 23794
 
4.7%
av 15630
 
3.1%
dr 14806
 
3.0%
to 10012
 
2.0%
fwy 5241
 
1.0%
off 5038
 
1.0%
on 4256
 
0.8%
Other values (7607) 234928
46.9%
2023-12-14T09:19:12.501530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
432549
17.7%
e 140265
 
5.7%
o 135481
 
5.6%
t 132740
 
5.4%
r 132620
 
5.4%
a 131464
 
5.4%
n 128126
 
5.3%
d 105694
 
4.3%
& 93021
 
3.8%
l 86494
 
3.5%
Other values (63) 921601
37.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1502380
61.6%
Space Separator 432549
 
17.7%
Uppercase Letter 390285
 
16.0%
Other Punctuation 93537
 
3.8%
Open Punctuation 9974
 
0.4%
Close Punctuation 9974
 
0.4%
Dash Punctuation 970
 
< 0.1%
Decimal Number 386
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 140265
 
9.3%
o 135481
 
9.0%
t 132740
 
8.8%
r 132620
 
8.8%
a 131464
 
8.8%
n 128126
 
8.5%
d 105694
 
7.0%
l 86494
 
5.8%
i 80837
 
5.4%
y 57446
 
3.8%
Other values (16) 371213
24.7%
Uppercase Letter
ValueCountFrequency (%)
R 67965
17.4%
S 62031
15.9%
H 35114
9.0%
A 27820
 
7.1%
M 22187
 
5.7%
B 21068
 
5.4%
W 20318
 
5.2%
D 18370
 
4.7%
C 17553
 
4.5%
L 13415
 
3.4%
Other values (16) 84444
21.6%
Decimal Number
ValueCountFrequency (%)
1 88
22.8%
0 68
17.6%
6 51
13.2%
2 45
11.7%
7 37
9.6%
5 31
 
8.0%
4 21
 
5.4%
3 20
 
5.2%
9 16
 
4.1%
8 9
 
2.3%
Other Punctuation
ValueCountFrequency (%)
& 93021
99.4%
: 257
 
0.3%
. 115
 
0.1%
/ 96
 
0.1%
' 48
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 9973
> 99.9%
[ 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 9973
> 99.9%
] 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
432549
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 970
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1892665
77.6%
Common 547390
 
22.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 140265
 
7.4%
o 135481
 
7.2%
t 132740
 
7.0%
r 132620
 
7.0%
a 131464
 
6.9%
n 128126
 
6.8%
d 105694
 
5.6%
l 86494
 
4.6%
i 80837
 
4.3%
R 67965
 
3.6%
Other values (42) 750979
39.7%
Common
ValueCountFrequency (%)
432549
79.0%
& 93021
 
17.0%
( 9973
 
1.8%
) 9973
 
1.8%
- 970
 
0.2%
: 257
 
< 0.1%
. 115
 
< 0.1%
/ 96
 
< 0.1%
1 88
 
< 0.1%
0 68
 
< 0.1%
Other values (11) 280
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2440055
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
432549
17.7%
e 140265
 
5.7%
o 135481
 
5.6%
t 132740
 
5.4%
r 132620
 
5.4%
a 131464
 
5.4%
n 128126
 
5.3%
d 105694
 
4.3%
& 93021
 
3.8%
l 86494
 
3.5%
Other values (63) 921601
37.8%

LONGITUDE
Real number (ℝ)

HIGH CORRELATION 

Distinct60258
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.03808
Minimum105.66151
Maximum128.91782
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1012.2 KiB
2023-12-14T09:19:12.583565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum105.66151
5-th percentile115.69315
Q1115.79621
median115.85523
Q3115.93419
95-th percentile116.83737
Maximum128.91782
Range23.256317
Interquartile range (IQR)0.137972

Descriptive statistics

Standard deviation1.0616833
Coefficient of variation (CV)0.0091494393
Kurtosis52.381959
Mean116.03808
Median Absolute Deviation (MAD)0.068805
Skewness6.4837667
Sum15032385
Variance1.1271715
MonotonicityNot monotonic
2023-12-14T09:19:12.642993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
115.730422 273
 
0.2%
115.812809 238
 
0.2%
115.938151 202
 
0.2%
115.997043 169
 
0.1%
115.917819 164
 
0.1%
116.014046 161
 
0.1%
115.945186 156
 
0.1%
115.83769 153
 
0.1%
115.854355 151
 
0.1%
115.842203 151
 
0.1%
Other values (60248) 127729
98.6%
ValueCountFrequency (%)
105.661505 1
< 0.1%
113.402441 1
< 0.1%
113.457191 1
< 0.1%
113.532843 1
< 0.1%
113.534861 1
< 0.1%
113.538132 2
< 0.1%
113.540271 1
< 0.1%
113.552467 1
< 0.1%
113.56344 1
< 0.1%
113.568052 1
< 0.1%
ValueCountFrequency (%)
128.917822 1
< 0.1%
128.917412 1
< 0.1%
128.875419 1
< 0.1%
128.857925 1
< 0.1%
128.85628 1
< 0.1%
128.841068 1
< 0.1%
128.812538 1
< 0.1%
128.79637 1
< 0.1%
128.78655 1
< 0.1%
128.777135 1
< 0.1%

LATITUDE
Real number (ℝ)

HIGH CORRELATION 

Distinct61903
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-31.842453
Minimum-35.08765
Maximum-10.449186
Zeros0
Zeros (%)0.0%
Negative129547
Negative (%)100.0%
Memory size1012.2 KiB
2023-12-14T09:19:12.699554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-35.08765
5-th percentile-33.336035
Q1-32.076102
median-31.966486
Q3-31.889587
95-th percentile-31.597344
Maximum-10.449186
Range24.638464
Interquartile range (IQR)0.186515

Descriptive statistics

Standard deviation1.7225462
Coefficient of variation (CV)-0.054095901
Kurtosis44.585816
Mean-31.842453
Median Absolute Deviation (MAD)0.09425
Skewness6.1723529
Sum-4125094.3
Variance2.9671654
MonotonicityNot monotonic
2023-12-14T09:19:12.761101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-31.670532 273
 
0.2%
-31.857112 238
 
0.2%
-32.062188 202
 
0.2%
-32.008137 169
 
0.1%
-32.088901 164
 
0.1%
-31.864203 161
 
0.1%
-32.026872 156
 
0.1%
-31.888965 152
 
0.1%
-32.065309 151
 
0.1%
-31.820966 151
 
0.1%
Other values (61893) 127730
98.6%
ValueCountFrequency (%)
-35.08765 1
< 0.1%
-35.083258 1
< 0.1%
-35.08166 1
< 0.1%
-35.078825 1
< 0.1%
-35.075294 1
< 0.1%
-35.069633 1
< 0.1%
-35.068295 1
< 0.1%
-35.067451 1
< 0.1%
-35.065815 1
< 0.1%
-35.062846 1
< 0.1%
ValueCountFrequency (%)
-10.449186 1
< 0.1%
-14.277027 1
< 0.1%
-14.291527 1
< 0.1%
-14.362412 1
< 0.1%
-14.409247 1
< 0.1%
-15.463731 1
< 0.1%
-15.471212 1
< 0.1%
-15.471334 1
< 0.1%
-15.485425 1
< 0.1%
-15.539414 1
< 0.1%
Distinct1826
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1012.2 KiB
Minimum2018-01-01 00:00:00
Maximum2022-12-31 00:00:00
2023-12-14T09:19:12.817747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:12.880677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

CRASH_TIME
Real number (ℝ)

Distinct1382
Distinct (%)1.1%
Missing111
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1334.4659
Minimum0
Maximum2359
Zeros233
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1012.2 KiB
2023-12-14T09:19:12.944774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile615
Q1950
median1410
Q31700
95-th percentile2045
Maximum2359
Range2359
Interquartile range (IQR)750

Descriptive statistics

Standard deviation465.53747
Coefficient of variation (CV)0.34885678
Kurtosis-0.33120419
Mean1334.4659
Median Absolute Deviation (MAD)320
Skewness-0.27837512
Sum1.7272792 × 108
Variance216725.13
MonotonicityNot monotonic
2023-12-14T09:19:13.000441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1530 2195
 
1.7%
1630 2157
 
1.7%
1600 1978
 
1.5%
1730 1948
 
1.5%
1700 1883
 
1.5%
1500 1881
 
1.5%
830 1591
 
1.2%
1800 1569
 
1.2%
1430 1562
 
1.2%
1230 1471
 
1.1%
Other values (1372) 111201
85.8%
ValueCountFrequency (%)
0 233
0.2%
1 11
 
< 0.1%
2 1
 
< 0.1%
3 5
 
< 0.1%
4 3
 
< 0.1%
5 72
 
0.1%
6 3
 
< 0.1%
7 1
 
< 0.1%
8 3
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
2359 21
 
< 0.1%
2358 1
 
< 0.1%
2357 3
 
< 0.1%
2356 4
 
< 0.1%
2355 63
< 0.1%
2354 2
 
< 0.1%
2353 4
 
< 0.1%
2352 2
 
< 0.1%
2351 1
 
< 0.1%
2350 92
0.1%

ACCIDENT_TYPE
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1012.2 KiB
Intersection
68560 
Midblock
60986 
Roads Open To Public Access
 
1

Length

Max length27
Median length12
Mean length10.117062
Min length8

Characters and Unicode

Total characters1310635
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMidblock
2nd rowMidblock
3rd rowIntersection
4th rowMidblock
5th rowIntersection

Common Values

ValueCountFrequency (%)
Intersection 68560
52.9%
Midblock 60986
47.1%
Roads Open To Public Access 1
 
< 0.1%

Length

2023-12-14T09:19:13.057191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T09:19:13.106015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
intersection 68560
52.9%
midblock 60986
47.1%
roads 1
 
< 0.1%
open 1
 
< 0.1%
to 1
 
< 0.1%
public 1
 
< 0.1%
access 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 137122
10.5%
n 137121
10.5%
t 137120
10.5%
c 129549
9.9%
o 129548
9.9%
i 129547
9.9%
s 68563
 
5.2%
I 68560
 
5.2%
r 68560
 
5.2%
l 60987
 
4.7%
Other values (13) 243958
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1181080
90.1%
Uppercase Letter 129551
 
9.9%
Space Separator 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 137122
11.6%
n 137121
11.6%
t 137120
11.6%
c 129549
11.0%
o 129548
11.0%
i 129547
11.0%
s 68563
5.8%
r 68560
5.8%
l 60987
5.2%
b 60987
5.2%
Other values (5) 121976
10.3%
Uppercase Letter
ValueCountFrequency (%)
I 68560
52.9%
M 60986
47.1%
R 1
 
< 0.1%
O 1
 
< 0.1%
T 1
 
< 0.1%
P 1
 
< 0.1%
A 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1310631
> 99.9%
Common 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 137122
10.5%
n 137121
10.5%
t 137120
10.5%
c 129549
9.9%
o 129548
9.9%
i 129547
9.9%
s 68563
 
5.2%
I 68560
 
5.2%
r 68560
 
5.2%
l 60987
 
4.7%
Other values (12) 243954
18.6%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1310635
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 137122
10.5%
n 137121
10.5%
t 137120
10.5%
c 129549
9.9%
o 129548
9.9%
i 129547
9.9%
s 68563
 
5.2%
I 68560
 
5.2%
r 68560
 
5.2%
l 60987
 
4.7%
Other values (13) 243958
18.6%

SEVERITY
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1012.2 KiB
PDO Major
69880 
PDO Minor
35727 
Medical
16669 
Hospital
 
6539
Fatal
 
732

Length

Max length9
Median length9
Mean length8.6695794
Min length5

Characters and Unicode

Total characters1123118
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHospital
2nd rowMedical
3rd rowPDO Minor
4th rowPDO Minor
5th rowMedical

Common Values

ValueCountFrequency (%)
PDO Major 69880
53.9%
PDO Minor 35727
27.6%
Medical 16669
 
12.9%
Hospital 6539
 
5.0%
Fatal 732
 
0.6%

Length

2023-12-14T09:19:13.156522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T09:19:13.205538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
pdo 105607
44.9%
major 69880
29.7%
minor 35727
 
15.2%
medical 16669
 
7.1%
hospital 6539
 
2.8%
fatal 732
 
0.3%

Most occurring characters

ValueCountFrequency (%)
M 122276
10.9%
o 112146
10.0%
P 105607
9.4%
D 105607
9.4%
O 105607
9.4%
105607
9.4%
r 105607
9.4%
a 94552
8.4%
j 69880
6.2%
i 58935
5.2%
Other values (10) 137294
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 571143
50.9%
Uppercase Letter 446368
39.7%
Space Separator 105607
 
9.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 112146
19.6%
r 105607
18.5%
a 94552
16.6%
j 69880
12.2%
i 58935
10.3%
n 35727
 
6.3%
l 23940
 
4.2%
e 16669
 
2.9%
d 16669
 
2.9%
c 16669
 
2.9%
Other values (3) 20349
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
M 122276
27.4%
P 105607
23.7%
D 105607
23.7%
O 105607
23.7%
H 6539
 
1.5%
F 732
 
0.2%
Space Separator
ValueCountFrequency (%)
105607
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1017511
90.6%
Common 105607
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 122276
12.0%
o 112146
11.0%
P 105607
10.4%
D 105607
10.4%
O 105607
10.4%
r 105607
10.4%
a 94552
9.3%
j 69880
6.9%
i 58935
5.8%
n 35727
 
3.5%
Other values (9) 101567
10.0%
Common
ValueCountFrequency (%)
105607
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1123118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 122276
10.9%
o 112146
10.0%
P 105607
9.4%
D 105607
9.4%
O 105607
9.4%
105607
9.4%
r 105607
9.4%
a 94552
8.4%
j 69880
6.2%
i 58935
5.2%
Other values (10) 137294
12.2%

EVENT_NATURE
Categorical

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)< 0.1%
Missing7767
Missing (%)6.0%
Memory size1012.2 KiB
Rear End
53896 
Right Angle
28084 
Sideswipe Same Dirn
15419 
Hit Object
9259 
Right Turn Thru
7943 
Other values (4)
7179 

Length

Max length19
Median length15
Mean length10.90496
Min length7

Characters and Unicode

Total characters1328006
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRight Angle
2nd rowRear End
3rd rowRight Angle
4th rowNon Collision
5th rowRear End

Common Values

ValueCountFrequency (%)
Rear End 53896
41.6%
Right Angle 28084
21.7%
Sideswipe Same Dirn 15419
 
11.9%
Hit Object 9259
 
7.1%
Right Turn Thru 7943
 
6.1%
Non Collision 3031
 
2.3%
Hit Pedestrian 1549
 
1.2%
Hit Animal 1312
 
1.0%
Head On 1287
 
1.0%
(Missing) 7767
 
6.0%

Length

2023-12-14T09:19:13.256163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T09:19:13.449112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
rear 53896
20.2%
end 53896
20.2%
right 36027
13.5%
angle 28084
10.5%
sideswipe 15419
 
5.8%
same 15419
 
5.8%
dirn 15419
 
5.8%
hit 12120
 
4.5%
object 9259
 
3.5%
turn 7943
 
3.0%
Other values (7) 19440
 
7.3%

Most occurring characters

ValueCountFrequency (%)
145142
 
10.9%
e 141881
 
10.7%
n 115552
 
8.7%
i 103327
 
7.8%
R 89923
 
6.8%
r 86750
 
6.5%
a 73463
 
5.5%
d 72151
 
5.4%
g 64111
 
4.8%
t 58955
 
4.4%
Other values (21) 376751
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 915942
69.0%
Uppercase Letter 266922
 
20.1%
Space Separator 145142
 
10.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 141881
15.5%
n 115552
12.6%
i 103327
11.3%
r 86750
9.5%
a 73463
8.0%
d 72151
7.9%
g 64111
7.0%
t 58955
6.4%
h 43970
 
4.8%
l 35458
 
3.9%
Other values (9) 120324
13.1%
Uppercase Letter
ValueCountFrequency (%)
R 89923
33.7%
E 53896
20.2%
S 30838
 
11.6%
A 29396
 
11.0%
T 15886
 
6.0%
D 15419
 
5.8%
H 13407
 
5.0%
O 10546
 
4.0%
N 3031
 
1.1%
C 3031
 
1.1%
Space Separator
ValueCountFrequency (%)
145142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1182864
89.1%
Common 145142
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 141881
12.0%
n 115552
 
9.8%
i 103327
 
8.7%
R 89923
 
7.6%
r 86750
 
7.3%
a 73463
 
6.2%
d 72151
 
6.1%
g 64111
 
5.4%
t 58955
 
5.0%
E 53896
 
4.6%
Other values (20) 322855
27.3%
Common
ValueCountFrequency (%)
145142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1328006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
145142
 
10.9%
e 141881
 
10.7%
n 115552
 
8.7%
i 103327
 
7.8%
R 89923
 
6.8%
r 86750
 
6.5%
a 73463
 
5.5%
d 72151
 
5.4%
g 64111
 
4.8%
t 58955
 
4.4%
Other values (21) 376751
28.4%

EVENT_TYPE
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing95881
Missing (%)74.0%
Memory size1012.2 KiB
Involving Overtaking
11302 
Involving Parking
9486 
Entering / Leaving Driveway
8398 
Involving Animal
2600 
Involving Pedestrian
1880 

Length

Max length27
Median length20
Mean length20.591933
Min length16

Characters and Unicode

Total characters693248
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntering / Leaving Driveway
2nd rowInvolving Overtaking
3rd rowEntering / Leaving Driveway
4th rowInvolving Animal
5th rowInvolving Animal

Common Values

ValueCountFrequency (%)
Involving Overtaking 11302
 
8.7%
Involving Parking 9486
 
7.3%
Entering / Leaving Driveway 8398
 
6.5%
Involving Animal 2600
 
2.0%
Involving Pedestrian 1880
 
1.5%
(Missing) 95881
74.0%

Length

2023-12-14T09:19:13.510110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T09:19:13.558184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
involving 25268
30.0%
overtaking 11302
13.4%
parking 9486
 
11.3%
entering 8398
 
10.0%
8398
 
10.0%
leaving 8398
 
10.0%
driveway 8398
 
10.0%
animal 2600
 
3.1%
pedestrian 1880
 
2.2%

Most occurring characters

ValueCountFrequency (%)
n 100998
14.6%
v 78634
11.3%
i 75730
10.9%
g 62852
9.1%
50462
 
7.3%
a 42064
 
6.1%
e 40256
 
5.8%
r 39464
 
5.7%
l 27868
 
4.0%
I 25268
 
3.6%
Other values (15) 149652
21.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 558658
80.6%
Uppercase Letter 75730
 
10.9%
Space Separator 50462
 
7.3%
Other Punctuation 8398
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 100998
18.1%
v 78634
14.1%
i 75730
13.6%
g 62852
11.3%
a 42064
7.5%
e 40256
 
7.2%
r 39464
 
7.1%
l 27868
 
5.0%
o 25268
 
4.5%
t 21580
 
3.9%
Other values (6) 43944
7.9%
Uppercase Letter
ValueCountFrequency (%)
I 25268
33.4%
P 11366
15.0%
O 11302
14.9%
E 8398
 
11.1%
L 8398
 
11.1%
D 8398
 
11.1%
A 2600
 
3.4%
Space Separator
ValueCountFrequency (%)
50462
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 8398
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 634388
91.5%
Common 58860
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 100998
15.9%
v 78634
12.4%
i 75730
11.9%
g 62852
9.9%
a 42064
 
6.6%
e 40256
 
6.3%
r 39464
 
6.2%
l 27868
 
4.4%
I 25268
 
4.0%
o 25268
 
4.0%
Other values (13) 115986
18.3%
Common
ValueCountFrequency (%)
50462
85.7%
/ 8398
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 693248
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 100998
14.6%
v 78634
11.3%
i 75730
10.9%
g 62852
9.1%
50462
 
7.3%
a 42064
 
6.1%
e 40256
 
5.8%
r 39464
 
5.7%
l 27868
 
4.0%
I 25268
 
3.6%
Other values (15) 149652
21.6%

TOTAL_BIKE_INVOLVED
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.021474832
Minimum0
Maximum9
Zeros126868
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size1012.2 KiB
2023-12-14T09:19:13.603466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.15304325
Coefficient of variation (CV)7.1266333
Kurtosis181.28857
Mean0.021474832
Median Absolute Deviation (MAD)0
Skewness9.2496915
Sum2782
Variance0.023422236
MonotonicityNot monotonic
2023-12-14T09:19:13.645174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 126868
97.9%
1 2598
 
2.0%
2 69
 
0.1%
3 10
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 126868
97.9%
1 2598
 
2.0%
2 69
 
0.1%
3 10
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
7 1
 
< 0.1%
3 10
 
< 0.1%
2 69
 
0.1%
1 2598
 
2.0%
0 126868
97.9%

TOTAL_TRUCK_INVOLVED
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1012.2 KiB
0
124820 
1
 
4616
2
 
111

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129547
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 124820
96.4%
1 4616
 
3.6%
2 111
 
0.1%

Length

2023-12-14T09:19:13.691171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T09:19:13.730970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 124820
96.4%
1 4616
 
3.6%
2 111
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 124820
96.4%
1 4616
 
3.6%
2 111
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 129547
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124820
96.4%
1 4616
 
3.6%
2 111
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 129547
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 124820
96.4%
1 4616
 
3.6%
2 111
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129547
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 124820
96.4%
1 4616
 
3.6%
2 111
 
0.1%

TOTAL_HEAVY_TRUCK_INVOLVED
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1012.2 KiB
0
127249 
1
 
2218
2
 
78
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129547
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 127249
98.2%
1 2218
 
1.7%
2 78
 
0.1%
3 2
 
< 0.1%

Length

2023-12-14T09:19:13.774194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T09:19:13.816937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 127249
98.2%
1 2218
 
1.7%
2 78
 
0.1%
3 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 127249
98.2%
1 2218
 
1.7%
2 78
 
0.1%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 129547
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 127249
98.2%
1 2218
 
1.7%
2 78
 
0.1%
3 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 129547
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 127249
98.2%
1 2218
 
1.7%
2 78
 
0.1%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129547
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 127249
98.2%
1 2218
 
1.7%
2 78
 
0.1%
3 2
 
< 0.1%

TOTAL_MOTOR_CYCLE_INVOLVED
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1012.2 KiB
0
125022 
1
 
4424
2
 
96
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129547
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 125022
96.5%
1 4424
 
3.4%
2 96
 
0.1%
3 5
 
< 0.1%

Length

2023-12-14T09:19:13.862628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T09:19:13.903562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 125022
96.5%
1 4424
 
3.4%
2 96
 
0.1%
3 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 125022
96.5%
1 4424
 
3.4%
2 96
 
0.1%
3 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 129547
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125022
96.5%
1 4424
 
3.4%
2 96
 
0.1%
3 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 129547
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125022
96.5%
1 4424
 
3.4%
2 96
 
0.1%
3 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129547
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125022
96.5%
1 4424
 
3.4%
2 96
 
0.1%
3 5
 
< 0.1%

TOTAL_OTHER_VEHICLES_INVOLVED
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6334998
Minimum0
Maximum8
Zeros8156
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size1012.2 KiB
2023-12-14T09:19:13.943789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.70411439
Coefficient of variation (CV)0.43104651
Kurtosis1.445121
Mean1.6334998
Median Absolute Deviation (MAD)0
Skewness-0.20350962
Sum211615
Variance0.49577708
MonotonicityNot monotonic
2023-12-14T09:19:13.988987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 76321
58.9%
1 38691
29.9%
0 8156
 
6.3%
3 5435
 
4.2%
4 781
 
0.6%
5 133
 
0.1%
6 24
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 8156
 
6.3%
1 38691
29.9%
2 76321
58.9%
3 5435
 
4.2%
4 781
 
0.6%
5 133
 
0.1%
6 24
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 4
 
< 0.1%
6 24
 
< 0.1%
5 133
 
0.1%
4 781
 
0.6%
3 5435
 
4.2%
2 76321
58.9%
1 38691
29.9%
0 8156
 
6.3%

TOTAL_PEDESTRIANS_INVOLVED
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1012.2 KiB
0
127427 
1
 
1958
2
 
145
3
 
15
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129547
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 127427
98.4%
1 1958
 
1.5%
2 145
 
0.1%
3 15
 
< 0.1%
4 2
 
< 0.1%

Length

2023-12-14T09:19:14.039432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-14T09:19:14.080632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 127427
98.4%
1 1958
 
1.5%
2 145
 
0.1%
3 15
 
< 0.1%
4 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 127427
98.4%
1 1958
 
1.5%
2 145
 
0.1%
3 15
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 129547
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 127427
98.4%
1 1958
 
1.5%
2 145
 
0.1%
3 15
 
< 0.1%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 129547
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 127427
98.4%
1 1958
 
1.5%
2 145
 
0.1%
3 15
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129547
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 127427
98.4%
1 1958
 
1.5%
2 145
 
0.1%
3 15
 
< 0.1%
4 2
 
< 0.1%

Interactions

2023-12-14T09:19:08.220704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:02.896557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.500490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.009819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.529382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.052111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.555139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.197433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.697748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.207804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.723320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:08.268858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:02.959499image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.546462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.055587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.575948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.097456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.601807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.241120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.742531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.253460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.765774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:08.315983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.021579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.589960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.102513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.622308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.144032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.653835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.284726image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.788265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.300562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.811022image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:08.367910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.095246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.638889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.151874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.669971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.192434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.701833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.333720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.837511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.351084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.857138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:08.418028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.173018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.684921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.199513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.716837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.239698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.749589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.379344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.883638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.398311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.903129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:08.466930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.217291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.729669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.247841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.762115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.282441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.794091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.424079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.927917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.440620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.948697image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:08.515253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.266002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.780463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.296251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.810341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.328356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.842256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.471944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.979330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.490904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.996663image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:08.563104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.312644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.826971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.342715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.858010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.373436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.889143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.516810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.024788image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.537083image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:08.040627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:08.614435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.359984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.872367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.389158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.906606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.417749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.059233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.562400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.068846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.584737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:08.084934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:08.661471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.403884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.915826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.434824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.953325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.460784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.102097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.603643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.113311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.629286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:08.130572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:08.711084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.447461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:03.959100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.478522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:04.998736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:05.505346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.147039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:06.648677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.158808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:07.675979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-14T09:19:08.171861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2023-12-14T09:19:14.124071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ACCIDENT_TYPEACC_IDCRASH_TIMECWAYEVENT_NATUREEVENT_TYPEINTERSECTION_NOLATITUDELONGITUDEOBJECTIDSEVERITYSLKTOTAL_BIKE_INVOLVEDTOTAL_HEAVY_TRUCK_INVOLVEDTOTAL_MOTOR_CYCLE_INVOLVEDTOTAL_OTHER_VEHICLES_INVOLVEDTOTAL_PEDESTRIANS_INVOLVEDTOTAL_TRUCK_INVOLVEDXY
ACCIDENT_TYPE1.000-0.007-0.0100.2670.2720.3990.004-0.0030.053-0.0000.0730.002-0.0160.0410.005-0.1390.0350.0380.053-0.003
ACC_ID-0.0071.000-0.0080.0140.0200.0200.017-0.001-0.0040.9540.0300.010-0.0060.0130.009-0.0250.0000.004-0.004-0.001
CRASH_TIME-0.010-0.0081.0000.0810.1230.177-0.0040.0010.007-0.0070.052-0.039-0.0350.0300.0080.0260.0170.0620.0070.001
CWAY0.2670.0140.0811.0000.2520.3710.0620.0090.073-0.0050.057-0.3960.0600.0250.028-0.1480.0340.0200.0730.009
EVENT_NATURE0.2720.0200.1230.2521.0000.7340.0370.016-0.0500.0060.186-0.1380.0870.0800.1290.1920.4350.067-0.0500.016
EVENT_TYPE0.3990.0200.1770.3710.7341.0000.0390.024-0.058-0.0100.212-0.189-0.1180.0920.042-0.0430.4230.075-0.0580.024
INTERSECTION_NO0.0040.017-0.0040.0620.0370.0391.0000.143-0.1070.0190.012-0.2400.0330.0290.018-0.0380.0060.019-0.1070.143
LATITUDE-0.003-0.0010.0010.0090.0160.0240.1431.0000.051-0.0030.070-0.0570.0030.0520.0200.0150.0150.0070.0511.000
LONGITUDE0.053-0.0040.0070.073-0.050-0.058-0.1070.0511.000-0.0040.0620.078-0.0390.0500.014-0.0490.0130.0031.0000.051
OBJECTID-0.0000.954-0.007-0.0050.006-0.0100.019-0.003-0.0041.0000.0260.004-0.0120.0120.012-0.1060.0080.014-0.004-0.003
SEVERITY0.0730.0300.0520.0570.1860.2120.0120.0700.0620.0261.000-0.1000.0130.0260.135-0.0130.0850.014-0.016-0.010
SLK0.0020.010-0.039-0.396-0.138-0.189-0.240-0.0570.0780.004-0.1001.000-0.0680.0920.0090.0060.0000.0140.078-0.057
TOTAL_BIKE_INVOLVED-0.016-0.006-0.0350.0600.087-0.1180.0330.003-0.039-0.0120.013-0.0681.0000.0060.010-0.1860.0070.011-0.0390.003
TOTAL_HEAVY_TRUCK_INVOLVED0.0410.0130.0300.0250.0800.0920.0290.0520.0500.0120.0260.0920.0061.0000.010-0.1690.0000.0000.059-0.005
TOTAL_MOTOR_CYCLE_INVOLVED0.0050.0090.0080.0280.1290.0420.0180.0200.0140.0120.1350.0090.0100.0101.000-0.2630.0000.0150.008-0.008
TOTAL_OTHER_VEHICLES_INVOLVED-0.139-0.0250.026-0.1480.192-0.043-0.0380.015-0.049-0.106-0.0130.006-0.186-0.169-0.2631.0000.0730.176-0.0490.015
TOTAL_PEDESTRIANS_INVOLVED0.0350.0000.0170.0340.4350.4230.0060.0150.0130.0080.0850.0000.0070.0000.0000.0731.0000.000-0.0040.010
TOTAL_TRUCK_INVOLVED0.0380.0040.0620.0200.0670.0750.0190.0070.0030.0140.0140.0140.0110.0000.0150.1760.0001.0000.024-0.004
X0.053-0.0040.0070.073-0.050-0.058-0.1070.0511.000-0.004-0.0160.078-0.0390.0590.008-0.049-0.0040.0241.0000.051
Y-0.003-0.0010.0010.0090.0160.0240.1431.0000.051-0.003-0.010-0.0570.003-0.005-0.0080.0150.010-0.0040.0511.000

Missing values

2023-12-14T09:19:08.875129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-14T09:19:09.172026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

XYOBJECTIDACC_IDROAD_NOROAD_NAMECOMMON_ROAD_NAMECWAYSLKINTERSECTION_NOINTERSECTION_DESCLONGITUDELATITUDECRASH_DATECRASH_TIMEACCIDENT_TYPESEVERITYEVENT_NATUREEVENT_TYPETOTAL_BIKE_INVOLVEDTOTAL_TRUCK_INVOLVEDTOTAL_HEAVY_TRUCK_INVOLVEDTOTAL_MOTOR_CYCLE_INVOLVEDTOTAL_OTHER_VEHICLES_INVOLVEDTOTAL_PEDESTRIANS_INVOLVED
0115.862061-32.1984474568704297471801050835Litoria DrLitoria DrS0.05NaNNaN115.862061-32.19844707/03/2018925.0MidblockHospitalRight AngleEntering / Leaving Driveway010010
1115.793506-32.177619456870439747186H002Melville Mandurah HwyRockingham RdR14.77NaNNaN115.793506-32.17761902/03/2018605.0MidblockMedicalRear EndNaN000020
2115.764571-31.8012354568704497471921310334Forrest RdForrest RdS0.9661194.0Forrest Rd & Gregory Av115.764571-31.80123512/03/2018850.0IntersectionPDO MinorRight AngleNaN000020
3115.751701-32.023429456870459747198H030Port Beach RdCurtin AvS2.78NaNNaN115.751701-32.02342912/03/20181715.0MidblockPDO MinorNon CollisionNaN000100
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129541115.853979-31.9056704581658398612031250063Latrobe StLatrobe StS0.5155339.0Latrobe St & Wellington Pde115.853979-31.90567002/09/2018400.0IntersectionPDO MinorHit ObjectNaN000000
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